Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble
Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble
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DOI：
10.1007/s11269-019-02370-0
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发表时间：
2019-09
影响因子：
4.3
通讯作者：
J. Niel;E. V. Uytven;Patrick Willems
中科院分区：
文献类型：
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作者：
J. Niel;E. V. Uytven;Patrick Willems
水资源管理人员在决策过程中面临着不断变化的气候，同时需要制定适应和减缓战略。然而，气候变化对世纪末的影响具有很大的不确定性，应对这一影响是决策者面临的一个巨大挑战。近年来，水文学家和气候学家的共同努力导致了许多气候变化对水资源影响的研究。然而，大多数研究只使用有限的集合规模和/或只关注一个贡献源，因此可能低估了总的不确定性。对于两个比利时集水区，我们模拟了五个不同的集总概念水文模型和10个不同的参数集，迫使24个全球气候模式的输出，涵盖了4种不同的排放情景，结合9种不同的降尺度方法，在参考（1961-1990）和未来（2071-2100）的时期，产生了一个大型的多模式集合41，850成员。结果表明，未来低流量和峰值流量都将变得更加极端，并且随着辐射强迫的增加，这些变化会更加强烈。低流量预测中最重要的不确定性来源是全球气候模型（解释总方差的27-36%）和水文模型结构（34-42%）。对于峰值流量预测，这些是全球气候模型（32-39%）和统计降尺度方法（21-26%）。此外，相互作用效应占不确定性的很大一部分（24-38%）。本研究的结果说明，当只关注多模式集成中的某些不确定性源时，可能会得到有偏差的结果和过于自信的结论。
Water managers are faced with a changing climate in the decision-making process while adaptation and mitigation strategies need to be developed. The climate change impact towards the end of the century, however, is highly uncertain and coping with this is a great challenge for decision makers. Over the recent years, combined efforts of hydrologists and climatologists have led to many climate change impact studies on water resources. However, most studies only use a limited ensemble size and/or focus on only one contributing source and hence possibly underestimate the total uncertainty.For two Belgian catchments, we simulated daily flow with five different lumped conceptual hydrological models and ten different parameter sets each, forced by the output of 24 global climate models covering four different emission scenarios, combined with 9 different downscaling methods over reference (1961–1990) and future (2071–2100) periods, resulting in a large multi-model ensemble with 41,850 members. Results show that both low and peak flows would become more extreme in the future, and these changes are stronger with increased radiative forcing. The most important uncertainty sources in low-flow projections are the global climate models (explaining 27–36% of the total variance) and the hydrological model structure (34–42%). For peak flow projections, these are global climate models (32–39%) and statistical downscaling methods (21–26%). Also, interaction effects account for a significant part of the uncertainty (24–38%). The results of this study illustrate that one might end up with biased results and overly confident conclusions when only focusing on some of the uncertainty sources in multi-model ensembles.